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Liang Y, Zhou K, Cao L. An advanced three-dimensional phenotypic measurement approach for extracting Ginkgo root structural parameters based on terrestrial laser scanning. FRONTIERS IN PLANT SCIENCE 2024; 15:1356078. [PMID: 39119499 PMCID: PMC11306031 DOI: 10.3389/fpls.2024.1356078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 07/04/2024] [Indexed: 08/10/2024]
Abstract
The phenotyping of plant roots is essential for improving plant productivity and adaptation. However, traditional techniques for assembling root phenotyping information are limited and often labor-intensive, especially for woody plants. In this study, an advanced approach called accurate and detailed quantitative structure model-based (AdQSM-based) root phenotypic measurement (ARPM) was developed to automatically extract phenotypes from Ginkgo tree root systems. The approach involves three-dimensional (3D) reconstruction of the point cloud obtained from terrestrial laser scanning (TLS) to extract key phenotypic parameters, including root diameter (RD), length, surface area, and volume. To evaluate the proposed method, two approaches [minimum spanning tree (MST)-based and triangulated irregular network (TIN)-based] were used to reconstruct the Ginkgo root systems from point clouds, and the number of lateral roots along with RD were extracted and compared with traditional methods. The results indicated that the RD extracted directly from point clouds [coefficient of determination (R 2) = 0.99, root-mean-square error (RMSE) = 0.41 cm] outperformed the results of 3D models (MST-based: R 2 = 0.71, RMSE = 2.20 cm; TIN-based: R 2 = 0.54, RMSE = 2.80 cm). Additionally, the MST-based model (F1 = 0.81) outperformed the TIN-based model (F1 = 0.80) in detecting the number of first-order and second-order lateral roots. Each phenotyping trait fluctuated with a different cloud parameter (CP), and the CP value of 0.002 (r = 0.94, p < 0.01) was found to be advantageous for better extraction of structural phenotypes. This study has helped with the extraction and quantitative analysis of root phenotypes and enhanced our understanding of the relationship between architectural parameters and corresponding physiological functions of tree roots.
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Affiliation(s)
| | | | - Lin Cao
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China
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2
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Yu J, Xu L, Shu Q, Luo S, Xi L. Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics. FRONTIERS IN PLANT SCIENCE 2024; 15:1361297. [PMID: 39036357 PMCID: PMC11258677 DOI: 10.3389/fpls.2024.1361297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 05/09/2024] [Indexed: 07/23/2024]
Abstract
Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC. Specifically, based on ATL08 (Land and Vegetation Height) product generated from Ice, Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) data and 80 measured plots, the NFCC values located at the LiDAR's footprint locations were predicted by the ML model. Based on the predicted NFCC, the spatial effects of NFCC were analyzed by Moran's I and semi-variogram. The results showed that (1) the Random Forest (RF) model had the strongest predicted performance among the built ML models (R2=0.75, RMSE=0.09); (2) the NFCC had a positive spatial correlation (Moran's I = 0.36), that is, the CC of adjacent natural forest footprints had similar trends or values, belonged to the spatial agglomeration distribution; the spatial variation was described by the exponential model (C0 = 0.12×10-2, C = 0.77×10-2, A0 = 10200 m); (3) topographic factors had significant effects on NFCC, among which elevation was the largest, slope was the second, and aspect was the least; (4) the NFCC spatial distribution obtained by SGCS was in great agreement with the footprint NFCC (R2 = 0.59). The predictions generated from the RF model constructed using ATL08 data offer a dependable data source for the spatial effects analysis.
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Affiliation(s)
- Jinge Yu
- College of Forestry, Southwest Forestry University, Kunming, China
| | - Li Xu
- College of Forestry, Southwest Forestry University, Kunming, China
| | - Qingtai Shu
- College of Forestry, Southwest Forestry University, Kunming, China
| | - Shaolong Luo
- College of Forestry, Southwest Forestry University, Kunming, China
| | - Lei Xi
- Institute of Ecological Protection and Restoration, Chinese Academy of Forestry, Beijing, China
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Nauber T, Hodač L, Wäldchen J, Mäder P. Parametrization of biological assumptions to simulate growth of tree branching architectures. TREE PHYSIOLOGY 2024; 44:tpae045. [PMID: 38696364 PMCID: PMC11128038 DOI: 10.1093/treephys/tpae045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/22/2024] [Accepted: 04/25/2024] [Indexed: 05/04/2024]
Abstract
Modeling and simulating the growth of the branching of tree species remains a challenge. With existing approaches, we can reconstruct or rebuild the branching architectures of real tree species, but the simulation of the growth process remains unresolved. First, we present a tree growth model to generate branching architectures that resemble real tree species. Secondly, we use a quantitative morphometric approach to infer the shape similarity of the generated simulations and real tree species. Within a functional-structural plant model, we implement a set of biological parameters that affect the branching architecture of trees. By modifying the parameter values, we aim to generate basic shapes of spruce, pine, oak and poplar. Tree shapes are compared using geometric morphometrics of landmarks that capture crown and stem outline shapes. Five biological parameters, namely xylem flow, shedding rate, proprioception, gravitysense and lightsense, most influenced the generated tree branching patterns. Adjusting these five parameters resulted in the different tree shapes of spruce, pine, oak, and poplar. The largest effect was attributed to gravity, as phenotypic responses to this effect resulted in different growth directions of gymnosperm and angiosperm branching architectures. Since we were able to obtain branching architectures that resemble real tree species by adjusting only a few biological parameters, our model is extendable to other tree species. Furthermore, the model will also allow the simulation of structural tree-environment interactions. Our simplifying approach to shape comparison between tree species, landmark geometric morphometrics, showed that even the crown-trunk outlines capture species differences based on their contrasting branching architectures.
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Affiliation(s)
- Tristan Nauber
- Data-intensive Systems and Visualization Group, Technische Universität Ilmenau, Ehrenbergstraße 29, Ilmenau 98693, Germany
| | - Ladislav Hodač
- Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena 07745, Germany
| | - Jana Wäldchen
- Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena 07745, Germany
- German Centre for Integrative Biodiversity Research, iDiv (Halle-Jena-Leipzig), Puschstraße 4, Leipzig 04103, Germany
| | - Patrick Mäder
- Data-intensive Systems and Visualization Group, Technische Universität Ilmenau, Ehrenbergstraße 29, Ilmenau 98693, Germany
- German Centre for Integrative Biodiversity Research, iDiv (Halle-Jena-Leipzig), Puschstraße 4, Leipzig 04103, Germany
- Faculty of Biological Sciences, Friedrich Schiller University Jena, Fürstengraben 1, Jena 07737, Germany
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4
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Nunes MH, Vaz MC, Camargo JLC, Laurance WF, de Andrade A, Vicentini A, Laurance S, Raumonen P, Jackson T, Zuquim G, Wu J, Peñuelas J, Chave J, Maeda EE. Edge effects on tree architecture exacerbate biomass loss of fragmented Amazonian forests. Nat Commun 2023; 14:8129. [PMID: 38097604 PMCID: PMC10721830 DOI: 10.1038/s41467-023-44004-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023] Open
Abstract
Habitat fragmentation could potentially affect tree architecture and allometry. Here, we use ground surveys of terrestrial LiDAR in Central Amazonia to explore the influence of forest edge effects on tree architecture and allometry, as well as forest biomass, 40 years after fragmentation. We find that young trees colonising the forest fragments have thicker branches and architectural traits that optimise for light capture, which result in 50% more woody volume than their counterparts of similar stem size and height in the forest interior. However, we observe a disproportionately lower height in some large trees, leading to a 30% decline in their woody volume. Despite the substantial wood production of colonising trees, the lower height of some large trees has resulted in a net loss of 6.0 Mg ha-1 of aboveground biomass - representing 2.3% of the aboveground biomass of edge forests. Our findings indicate a strong influence of edge effects on tree architecture and allometry, and uncover an overlooked factor that likely exacerbates carbon losses in fragmented forests.
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Affiliation(s)
- Matheus Henrique Nunes
- Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland.
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA.
| | - Marcel Caritá Vaz
- Institute for Environmental Science and Sustainabilty, Wilkes University, Wilkes-Barre, PA, USA
| | - José Luís Campana Camargo
- Ecology Graduate Program, National Institute for Amazonian Research, (INPA), Manaus, Brazil
- Biological Dynamics of Forest Fragments Project (BDFFP) at National Institute for Amazonian Research (INPA), Manaus, Brazil
| | - William F Laurance
- Centre for Tropical Environmental and Sustainability Science, College of Science and Engineering, James Cook University, Cairns, Queensland, Australia
| | - Ana de Andrade
- Biological Dynamics of Forest Fragments Project (BDFFP) at National Institute for Amazonian Research (INPA), Manaus, Brazil
| | - Alberto Vicentini
- Biological Dynamics of Forest Fragments Project (BDFFP) at National Institute for Amazonian Research (INPA), Manaus, Brazil
- Coordenação de Pesquisas em Ecologia, Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, AM, Brasil
| | - Susan Laurance
- Centre for Tropical Environmental and Sustainability Science, College of Science and Engineering, James Cook University, Cairns, Queensland, Australia
| | - Pasi Raumonen
- Computing Sciences, Tampere University, Tampere, Finland
| | - Toby Jackson
- Plant Sciences and Conservation Research Institute, University of Cambridge, Cambridge, United Kingdom
| | - Gabriela Zuquim
- Amazon Research Team, Department of Biology, University of Turku, Turku, Finland
| | - Jin Wu
- School of Biological Sciences and Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong, China
| | - Josep Peñuelas
- CREAF, Cerdanyola del Vallès, Barcelona, Catalonia, Spain
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Barcelona, Catalonia, Spain
| | - Jérôme Chave
- Laboratoire Evolution et Diversité Biologique, CNRS, UPS, IRD, Université Paul Sabatier, Toulouse, France
| | - Eduardo Eiji Maeda
- Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland.
- Finnish Meteorological Institute, FMI, Helsinki, Finland.
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Cushman KC, Albert LP, Norby RJ, Saatchi S. Innovations in plant science from integrative remote sensing research: an introduction to a Virtual Issue. THE NEW PHYTOLOGIST 2023; 240:1707-1711. [PMID: 37915249 DOI: 10.1111/nph.19237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 11/03/2023]
Abstract
This article is an Editorial to the Virtual issue on ‘Remote sensing’ that includes the following papers Chavana‐Bryant et al. (2017), Coupel‐Ledru et al. (2022), Cushman & Machado (2020), Disney (2019), D'Odorico et al. (2020), Dong et al. (2022), Fischer et al. (2019), Gamon et al. (2023), Gu et al. (2019), Guillemot et al. (2020), Jucker (2021), Koh et al. (2022), Konings et al. (2019), Kothari et al. (2023), Martini et al. (2022), Richardson (2019), Santini et al. (2021), Schimel et al. (2019), Serbin et al. (2019), Smith et al. (2019, 2020), Still et al. (2021), Stovall et al. (2021), Wang et al. (2020), Wong et al. (2020), Wu et al. (2021), Wu et al. (2017), Wu et al. (2018), Wu et al. (2019), Xu et al. (2021), Yan et al. (2021). Access the Virtual Issue at www.newphytologist.com/virtualissues.
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Affiliation(s)
- K C Cushman
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
| | - Loren P Albert
- College of Forestry, Oregon State University, Corvallis, OR, 97331, USA
| | - Richard J Norby
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, 37996, USA
| | - Sassan Saatchi
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
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Lin Y, Filin S, Billen R, Mizoue N. Co-developing an international TLS network for the 3D ecological understanding of global trees: System architecture, remote sensing models, and functional prospects. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2023; 16:100257. [PMID: 36941885 PMCID: PMC10024182 DOI: 10.1016/j.ese.2023.100257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Trees are spread worldwide, as the watchmen that experience the intricate ecological effects caused by various environmental factors. In order to better understand such effects, it is preferential to achieve finely and fully mapped global trees and their environments. For this task, aerial and satellite-based remote sensing (RS) methods have been developed. However, a critical branch regarding the apparent forms of trees has significantly fallen behind due to the technical deficiency found within their global-scale surveying methods. Now, terrestrial laser scanning (TLS), a state-of-the-art RS technology, is useful for the in situ three-dimensional (3D) mapping of trees and their environments. Thus, we proposed co-developing an international TLS network as a macroscale ecotechnology to increase the 3D ecological understanding of global trees. First, we generated the system architecture and tested the available RS models to deepen its ground stakes. Then, we verified the ecotechnology regarding the identification of its theoretical feasibility, a review of its technical preparations, and a case testification based on a prototype we designed. Next, we conducted its functional prospects by previewing its scientific and technical potentials and its functional extensibility. Finally, we summarized its technical and scientific challenges, which can be used as the cutting points to promote the improvement of this technology in future studies. Overall, with the implication of establishing a novel cornerstone-sense ecotechnology, the co-development of an international TLS network can revolutionize the 3D ecological understanding of global trees and create new fields of research from 3D global tree structural ecology to 3D macroecology.
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Affiliation(s)
- Yi Lin
- School of Earth and Space Sciences, Peking University, Beijing, 100871, China
| | - Sagi Filin
- Technion – Israel Institute of Technology, Haifa IL, 32000, Israel
| | - Roland Billen
- Department of Geography, University of Liège, Liège, 4000, Belgium
| | - Nobuya Mizoue
- Faculty of Agriculture, Kyushu University, Fukuoka, 819-0395, Japan
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Zang J, Jin S, Zhang S, Li Q, Mu Y, Li Z, Li S, Wang X, Su Y, Jiang D. Field-measured canopy height may not be as accurate and heritable as believed: evidence from advanced 3D sensing. PLANT METHODS 2023; 19:39. [PMID: 37009892 PMCID: PMC10069135 DOI: 10.1186/s13007-023-01012-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
Canopy height (CH) is an important trait for crop breeding and production. The rapid development of 3D sensing technologies shed new light on high-throughput height measurement. However, a systematic comparison of the accuracy and heritability of different 3D sensing technologies is seriously lacking. Moreover, it is questionable whether the field-measured height is as reliable as believed. This study uncovered these issues by comparing traditional height measurement with four advanced 3D sensing technologies, including terrestrial laser scanning (TLS), backpack laser scanning (BLS), gantry laser scanning (GLS), and digital aerial photogrammetry (DAP). A total of 1920 plots covering 120 varieties were selected for comparison. Cross-comparisons of different data sources were performed to evaluate their performances in CH estimation concerning different CH, leaf area index (LAI), and growth stage (GS) groups. Results showed that 1) All 3D sensing data sources had high correlations with field measurement (r > 0.82), while the correlations between different 3D sensing data sources were even better (r > 0.87). 2) The prediction accuracy between different data sources decreased in subgroups of CH, LAI, and GS. 3) Canopy height showed high heritability from all datasets, and 3D sensing datasets had even higher heritability (H2 = 0.79-0.89) than FM (field measurement) (H2 = 0.77). Finally, outliers of different datasets are analyzed. The results provide novel insights into different methods for canopy height measurement that may ensure the high-quality application of this important trait.
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Affiliation(s)
- Jingrong Zang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored By Province and Ministry, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored By Province and Ministry, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.
| | - Songyin Zhang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored By Province and Ministry, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Qing Li
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored By Province and Ministry, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yue Mu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored By Province and Ministry, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Ziyu Li
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored By Province and Ministry, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Shaochen Li
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored By Province and Ministry, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Xiao Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored By Province and Ministry, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yanjun Su
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored By Province and Ministry, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
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Ferrara C, Puletti N, Guasti M, Scotti R. Mapping Understory Vegetation Density in Mediterranean Forests: Insights from Airborne and Terrestrial Laser Scanning Integration. SENSORS (BASEL, SWITZERLAND) 2023; 23:511. [PMID: 36617109 PMCID: PMC9824637 DOI: 10.3390/s23010511] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 12/27/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
The understory is an essential ecological and structural component of forest ecosystems. The lack of efficient, accurate, and objective methods for evaluating and quantifying the spatial spread of understory characteristics over large areas is a challenge for forest planning and management, with specific regard to biodiversity and habitat governance. In this study, we used terrestrial and airborne laser scanning (TLS and ALS) data to characterize understory in a European beech and black pine forest in Italy. First, we linked understory structural features derived from traditional field measurements with TLS metrics, then, we related such metrics to the ones derived from ALS. Results indicate that (i) the upper understory density (5-10 m above ground) is significantly associated with two ALS metrics, specifically the mean height of points belonging to the lower third of the ALS point cloud within the voxel (HM1/3) and the corresponding standard deviation (SD1/3), while (ii) for the lower understory layer (2-5 m above ground), the most related metric is HM1/3 alone. As an example application, we have produced a map of forest understory for each layer, extending over the entire study region covered by ALS data, based on the developed spatial prediction models. With this study, we also demonstrated the power of hand-held mobile-TLS as a fast and high-resolution tool for measuring forest structural attributes and obtaining relevant ecological data.
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Affiliation(s)
- Carlotta Ferrara
- CREA, Research Centre for Forestry and Wood, Via Valle della Quistione, IT-00166 Rome, Italy
| | - Nicola Puletti
- CREA, Research Centre for Forestry and Wood, Viale Santa Margherita 80, IT-52100 Arezzo, Italy
| | - Matteo Guasti
- CREA, Research Centre for Forestry and Wood, Viale Santa Margherita 80, IT-52100 Arezzo, Italy
| | - Roberto Scotti
- UNISS, Department of agriculture, NuoroForestrySchool, Via C. Colombo 1, IT-08100 Nuoro, Italy
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9
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Jucker T, Fischer FJ, Chave J, Coomes DA, Caspersen J, Ali A, Loubota Panzou GJ, Feldpausch TR, Falster D, Usoltsev VA, Adu‐Bredu S, Alves LF, Aminpour M, Angoboy IB, Anten NPR, Antin C, Askari Y, Muñoz R, Ayyappan N, Balvanera P, Banin L, Barbier N, Battles JJ, Beeckman H, Bocko YE, Bond‐Lamberty B, Bongers F, Bowers S, Brade T, van Breugel M, Chantrain A, Chaudhary R, Dai J, Dalponte M, Dimobe K, Domec J, Doucet J, Duursma RA, Enríquez M, van Ewijk KY, Farfán‐Rios W, Fayolle A, Forni E, Forrester DI, Gilani H, Godlee JL, Gourlet‐Fleury S, Haeni M, Hall JS, He J, Hemp A, Hernández‐Stefanoni JL, Higgins SI, Holdaway RJ, Hussain K, Hutley LB, Ichie T, Iida Y, Jiang H, Joshi PR, Kaboli H, Larsary MK, Kenzo T, Kloeppel BD, Kohyama T, Kunwar S, Kuyah S, Kvasnica J, Lin S, Lines ER, Liu H, Lorimer C, Loumeto J, Malhi Y, Marshall PL, Mattsson E, Matula R, Meave JA, Mensah S, Mi X, Momo S, Moncrieff GR, Mora F, Nissanka SP, O'Hara KL, Pearce S, Pelissier R, Peri PL, Ploton P, Poorter L, Pour MJ, Pourbabaei H, Dupuy‐Rada JM, Ribeiro SC, Ryan C, Sanaei A, Sanger J, Schlund M, Sellan G, Shenkin A, Sonké B, Sterck FJ, Svátek M, Takagi K, Trugman AT, Ullah F, Vadeboncoeur MA, Valipour A, Vanderwel MC, Vovides AG, Wang W, Wang L, Wirth C, Woods M, Xiang W, Ximenes FDA, Xu Y, Yamada T, Zavala MA. Tallo: A global tree allometry and crown architecture database. GLOBAL CHANGE BIOLOGY 2022; 28:5254-5268. [PMID: 35703577 PMCID: PMC9542605 DOI: 10.1111/gcb.16302] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/12/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.
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Affiliation(s)
- Tommaso Jucker
- School of Biological SciencesUniversity of BristolBristolUK
| | | | - Jérôme Chave
- Laboratoire Évolution et Diversité Biologique (EDB)UMR 5174 (CNRS/IRD/UPS)Toulouse Cedex 9France
- Université ToulouseToulouse Cedex 9France
| | - David A. Coomes
- Conservation Research InstituteUniversity of CambridgeCambridgeUK
| | - John Caspersen
- Institute of Forestry and ConservationUniversity of TorontoTorontoOntarioCanada
| | - Arshad Ali
- Forest Ecology Research Group, College of Life SciencesHebei UniversityBaodingHebeiChina
| | - Grace Jopaul Loubota Panzou
- Université de Liège, Gembloux Agro‐Bio TechGemblouxBelgium
- Laboratoire de Biodiversité, de Gestion des Ecosystèmes et de l'Environnement (LBGE), Faculté des Sciences et TechniquesUniversité Marien NgouabiBrazzavilleRepublic of Congo
| | - Ted R. Feldpausch
- College of Life and Environmental SciencesUniversity of ExeterExeterUK
| | - Daniel Falster
- Evolution & Ecology Research CentreUniversity of New South Wales SydneySydneyNew South WalesAustralia
| | - Vladimir A. Usoltsev
- Department of ForestryUral State Forest Engineering UniversityYekaterinburgRussia
- Department of Forest DynamicsBotanical Garden of the Ural Branch of Russian Academy of SciencesYekaterinburgRussia
| | - Stephen Adu‐Bredu
- Forestry Research Institute of Ghana, Council for Scientific and Industrial ResearchUniversityKumasiGhana
| | - Luciana F. Alves
- Center for Tropical Research, Institute of the Environment and SustainabilityUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Mohammad Aminpour
- Natural Recourses and Watershed Management Office, West Azerbaijan ProvinceUrmiaIran
| | - Ilondea B. Angoboy
- Institut National pour l'Etude et la Recherche AgronimiquesDemocratic Republic of the Congo
| | - Niels P. R. Anten
- Center for Crop Systems AnalysisWageningen UniversityWageningenThe Netherlands
| | - Cécile Antin
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
| | - Yousef Askari
- Research Division of Natural Resources, Kohgiluyeh and Boyerahmad Agriculture and Natural Resources Research and Education Center, AREEOYasoujIran
| | - Rodrigo Muñoz
- Departamento de Ecología y Recursos Naturales, Facultad de CienciasUniversidad Nacional Autónoma de México, CoyoacánCiudad de MéxicoMexico
- Forest Ecology and Forest Management GroupWageningen UniversityWageningenThe Netherlands
| | | | - Patricia Balvanera
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de MéxicoMoreliaMichoacánMexico
| | | | - Nicolas Barbier
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
| | | | - Hans Beeckman
- Service of Wood BiologyRoyal Museum for Central AfricaTervurenBelgium
| | - Yannick E. Bocko
- Laboratoire de Biodiversité, de Gestion des Ecosystèmes et de l'Environnement (LBGE), Faculté des Sciences et TechniquesUniversité Marien NgouabiBrazzavilleRepublic of Congo
| | - Ben Bond‐Lamberty
- Pacific Northwest National LaboratoryJoint Global Change Research InstituteCollege ParkMarylandUSA
| | - Frans Bongers
- Forest Ecology and Forest Management GroupWageningen UniversityWageningenThe Netherlands
| | - Samuel Bowers
- School of GeoSciencesUniversity of EdinburghEdinburghUK
| | - Thomas Brade
- School of GeoSciencesUniversity of EdinburghEdinburghUK
| | - Michiel van Breugel
- Yale‐NUS CollegeSingapore
- ForestGEOSmithsonian Tropical Research InstituteApartadoPanamaRepublic of Panama
- Department of GeographyNational University of SingaporeSingapore
| | | | - Rajeev Chaudhary
- Division Forest OfficeMinistry of ForestDhangadhiSudurpashchim ProvinceNepal
| | - Jingyu Dai
- College of Urban and Environmental Sciences and MOE Laboratory for Earth Surface ProcessesPeking UniversityBeijingChina
| | - Michele Dalponte
- Research and Innovation Centre, Fondazione Edmund MachSan Michele all'AdigeItaly
| | - Kangbéni Dimobe
- Institut des Sciences de l'Environnement et du Développement Rural (ISEDR)Université de DédougouDédougouBurkina Faso
| | - Jean‐Christophe Domec
- Bordeaux Sciences Agro‐UMR ISPA, INRAEBordeauxFrance
- Nicholas School of the EnvironmentDuke UniversityDurhamNCUSA
| | | | | | - Moisés Enríquez
- Departamento de Ecología y Recursos Naturales, Facultad de CienciasUniversidad Nacional Autónoma de México, CoyoacánCiudad de MéxicoMexico
| | - Karin Y. van Ewijk
- Department of Geography and Planning, Queen's UniversityKingstonOntarioCanada
| | | | | | - Eric Forni
- CIRAD, UPR Forêts et SociétésMontpellierFrance
| | | | - Hammad Gilani
- Institute of Space Technology, Islamabad HighwayIslamabadPakistan
| | | | | | - Matthias Haeni
- Swiss Federal Research Institute WSLBirmensdorfSwitzerland
| | - Jefferson S. Hall
- ForestGEOSmithsonian Tropical Research InstituteApartadoPanamaRepublic of Panama
| | - Jie‐Kun He
- Spatial Ecology Lab, School of Life SciencesSouth China Normal UniversityGuangzhouGuangdongChina
| | - Andreas Hemp
- Department of Plant SystematicsUniversity of BayreuthBayreuthGermany
| | | | | | | | - Kiramat Hussain
- Gilgit‐Baltistan Forest Wildlife and Environment DepartmentGilgitPakistan
| | - Lindsay B. Hutley
- Research Institute for the Environment & LivelihoodsCharles Darwin UniversityCasuarinaNorthern TerritoryAustralia
| | - Tomoaki Ichie
- Faculty of Agriculture and Marine ScienceKochi UniversityNankokuKochiJapan
| | - Yoshiko Iida
- Forestry and Forest Products Research InstituteTsukubaIbarakiJapan
| | - Hai‐sheng Jiang
- Spatial Ecology Lab, School of Life SciencesSouth China Normal UniversityGuangzhouGuangdongChina
| | | | - Hasan Kaboli
- Faculty of Desert Studies Semnan UniversitySemnanIran
| | | | - Tanaka Kenzo
- Japan International Research Center for Agricultural SciencesTsukubaIbarakiJapan
| | - Brian D. Kloeppel
- Department of Geosciences and Natural ResourcesWestern Carolina UniversityCullowheeNorth CarolinaUSA
- Graduate School and ResearchWestern Carolina UnversityCullowheeNorth CarolinaUSA
| | - Takashi Kohyama
- Faculty of Environmental Earth ScienceHokkaido UniversitySapporoJapan
| | - Suwash Kunwar
- Division Forest OfficeMinistry of ForestDhangadhiSudurpashchim ProvinceNepal
- Department of Forest Resources Management, College of ForestryNanjing Forestry UniversityNanjingJiangsuChina
| | - Shem Kuyah
- Jomo Kenyatta University of Agriculture and Technology (JKUAT)NairobiKenya
| | - Jakub Kvasnica
- Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood TechnologyMendel University in BrnoBrnoCzech Republic
| | - Siliang Lin
- Guangdong Provincial Key Laboratory of High Technology for Plant Protection, Plant Protection Research InstituteGuangdong Academy of Agricultural SciencesGuangzhouGuangdongChina
| | - Emily R. Lines
- Department of GeographyUniversity of CambridgeCambridgeUK
| | - Hongyan Liu
- College of Urban and Environmental Sciences and MOE Laboratory for Earth Surface ProcessesPeking UniversityBeijingChina
| | - Craig Lorimer
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Jean‐Joël Loumeto
- Laboratoire de Biodiversité, de Gestion des Ecosystèmes et de l'Environnement (LBGE), Faculté des Sciences et TechniquesUniversité Marien NgouabiBrazzavilleRepublic of Congo
| | - Yadvinder Malhi
- Environmental Change Institute, School of Geography and the EnvironmentUniversity of OxfordOxfordUK
| | - Peter L. Marshall
- Faculty of ForestryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Eskil Mattsson
- IVL Swedish Environmental Research InstituteGöteborgSweden
- Gothenburg Global Biodiversity Centre (GGBC), GothenburgSweden
| | - Radim Matula
- Faculty of Forestry and Wood SciencesCzech University of Life Sciences Prague, Prague 6SuchdolCzech Republic
| | - Jorge A. Meave
- Departamento de Ecología y Recursos Naturales, Facultad de CienciasUniversidad Nacional Autónoma de México, CoyoacánCiudad de MéxicoMexico
| | - Sylvanus Mensah
- Laboratoire de Biomathématiques et d'Estimations Forestières, Faculté des Sciences AgronomiquesUniversité d'Abomey CalaviCotonouBenin
| | - Xiangcheng Mi
- State Key Laboratory of Vegetation and Environmental Change, Institute of BotanyChinese Academy of SciencesBeijingChina
| | - Stéphane Momo
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
- Laboratoire de Botanique systématique et d'Ecologie, Département des Sciences Biologiques, Ecole Normale SupérieureUniversité de Yaoundé IYaoundéCameroon
| | - Glenn R. Moncrieff
- Fynbos Node, South African Environmental Observation NetworkClaremontSouth Africa
- Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical SciencesUniversity of Cape TownRondeboschSouth Africa
| | - Francisco Mora
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de MéxicoMoreliaMichoacánMexico
| | - Sarath P. Nissanka
- Department of Crop Science, Faculty of AgricultureUniversity of PeradeniyaPeradeniyaSri Lanka
| | | | | | - Raphaël Pelissier
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
| | - Pablo L. Peri
- Universidad Nacional de la Patagonia Austral (UNPA) ‐ Instituto Nacional de Tecnología Agropecuaria (INTA) ‐ CONICETRío GallegosSanta CruzArgentina
| | - Pierre Ploton
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
| | - Lourens Poorter
- Forest Ecology and Forest Management GroupWageningen UniversityWageningenThe Netherlands
| | | | - Hassan Pourbabaei
- Department of Forestry, Faculty of Natural ResourcesUniversity of GuilanSomehsaraIran
| | - Juan Manuel Dupuy‐Rada
- Centro de Investigación Científica de Yucatán A.C., Unidad de Recursos NaturalesMéridaYucatánMexico
| | - Sabina C. Ribeiro
- Centro de Ciências Biológicas e da NaturezaUniversidade Federal do Acre, Campus UniversitárioRio BrancoBrazil
| | - Casey Ryan
- School of GeoSciencesUniversity of EdinburghEdinburghUK
| | - Anvar Sanaei
- Systematic Botany and Functional Biodiversity, Institute of BiologyLeipzig UniversityLeipzigGermany
| | | | - Michael Schlund
- Department of Natural Resources, Faculty of Geo‐information Science and Earth Observation (ITC)University of TwenteEnschedeThe Netherlands
| | - Giacomo Sellan
- UMR EcoFoG, CNRSKourouFrench Guiana
- Department of Natural SciencesManchester Metropolitan UniversityManchesterUK
| | - Alexander Shenkin
- Environmental Change Institute, School of Geography and the EnvironmentUniversity of OxfordOxfordUK
| | - Bonaventure Sonké
- Laboratoire de Botanique systématique et d'Ecologie, Département des Sciences Biologiques, Ecole Normale SupérieureUniversité de Yaoundé IYaoundéCameroon
| | - Frank J. Sterck
- Forest Ecology and Forest Management GroupWageningen UniversityWageningenThe Netherlands
| | - Martin Svátek
- Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood TechnologyMendel University in BrnoBrnoCzech Republic
| | - Kentaro Takagi
- Field Science Center for Northern BiosphereHokkaido UniversityHoronobeJapan
| | - Anna T. Trugman
- Department of GeographyUniversity of California Santa BarbaraSanta BarbaraCaliforniaUSA
| | - Farman Ullah
- Forest Ecology Research Group, College of Life SciencesHebei UniversityBaodingHebeiChina
- Department of Forest Resources Management, College of ForestryNanjing Forestry UniversityNanjingJiangsuChina
| | | | - Ahmad Valipour
- Department of Forestry and The Center for Research and Development of Northern Zagros ForestryUniversity of KurdistanErbilIran
| | | | - Alejandra G. Vovides
- School of Geographical and Earth SciencesUniversity of Glasgow, East QuadrangleGlasgowUK
| | - Weiwei Wang
- State Key Laboratory of Vegetation and Environmental Change, Institute of BotanyChinese Academy of SciencesBeijingChina
| | - Li‐Qiu Wang
- Department of Forest Resources Management, College of ForestryNanjing Forestry UniversityNanjingJiangsuChina
| | - Christian Wirth
- Systematic Botany and Functional Biodiversity, Institute of BiologyUniversity of LeipzigLeipzigGermany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
| | - Murray Woods
- Ontario Ministry of Natural ResourcesNorth BayOntarioCanada
| | - Wenhua Xiang
- Faculty of Life Science and TechnologyCentral South University of Forestry and TechnologyChangshaHunanChina
| | | | - Yaozhan Xu
- State Key Laboratory of Aquatic Botany and Watershed EcologyWuhan Botanical Garden, Chinese Academy of SciencesWuhanChina
- Center of Conservation Biology, Core Botanical GardensChinese Academy of SciencesWuhanChina
| | - Toshihiro Yamada
- Graduate School of Integrated Sciences of LifeHiroshima UniversityHiroshimaJapan
| | - Miguel A. Zavala
- Forest Ecology and Restoration Group (FORECO), Departamento de Ciencias de la VidaUniversidad de AlcaláMadridSpain
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10
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Lin Y, Hyyppä J. Towards 3D basic theories of plant forms. Commun Biol 2022; 5:703. [PMID: 35835949 PMCID: PMC9283379 DOI: 10.1038/s42003-022-03652-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 06/29/2022] [Indexed: 11/25/2022] Open
Abstract
Allometric, metabolic, and biomechanical theories are the critical foundations for scientifically deciphering plant forms. Their concrete laws, however, are found to deviate for plenty of plant specimens. This phenomenon has not been extensively studied, due to technical restrictions. This bottleneck now can be overcome by the state-of-the-art three-dimensional (3D) mapping technologies, such as fine-scale terrestrial laser scanning. On these grounds, we proposed to reexamine the basic theories regarding plant forms, and then, we case validated the feasibility of upgrading them into 3D modes. As an in-time enlightening of 3D revolutionizing the related basic subject, our theoretical prospect further sorted out the potential challenges as the cutting points for advancing its future exploration, which may enable 3D reconstruction of the basic theories of plant forms and even boost life science. In this Perspective, the authors discuss how state-of-the-art three-dimensional mapping technologies such as fine-scale terrestrial laser scanning can help us understand the theories of plant forms.
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Affiliation(s)
- Yi Lin
- School of Earth and Space Sciences, Peking University, Beijing, 100871, China.
| | - Juha Hyyppä
- Finnish Geospatial Research Institute, FI-02430, Masala, Finland
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11
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Martin De Kauwe. THE NEW PHYTOLOGIST 2022; 235:18-19. [PMID: 35652269 DOI: 10.1111/nph.18168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
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12
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Piponiot C, Anderson-Teixeira KJ, Davies SJ, Allen D, Bourg NA, Burslem DFRP, Cárdenas D, Chang-Yang CH, Chuyong G, Cordell S, Dattaraja HS, Duque Á, Ediriweera S, Ewango C, Ezedin Z, Filip J, Giardina CP, Howe R, Hsieh CF, Hubbell SP, Inman-Narahari FM, Itoh A, Janík D, Kenfack D, Král K, Lutz JA, Makana JR, McMahon SM, McShea W, Mi X, Bt Mohamad M, Novotný V, O'Brien MJ, Ostertag R, Parker G, Pérez R, Ren H, Reynolds G, Md Sabri MD, Sack L, Shringi A, Su SH, Sukumar R, Sun IF, Suresh HS, Thomas DW, Thompson J, Uriarte M, Vandermeer J, Wang Y, Ware IM, Weiblen GD, Whitfeld TJS, Wolf A, Yao TL, Yu M, Yuan Z, Zimmerman JK, Zuleta D, Muller-Landau HC. Distribution of biomass dynamics in relation to tree size in forests across the world. THE NEW PHYTOLOGIST 2022; 234:1664-1677. [PMID: 35201608 DOI: 10.1111/nph.17995] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 10/05/2021] [Indexed: 06/14/2023]
Abstract
Tree size shapes forest carbon dynamics and determines how trees interact with their environment, including a changing climate. Here, we conduct the first global analysis of among-site differences in how aboveground biomass stocks and fluxes are distributed with tree size. We analyzed repeat tree censuses from 25 large-scale (4-52 ha) forest plots spanning a broad climatic range over five continents to characterize how aboveground biomass, woody productivity, and woody mortality vary with tree diameter. We examined how the median, dispersion, and skewness of these size-related distributions vary with mean annual temperature and precipitation. In warmer forests, aboveground biomass, woody productivity, and woody mortality were more broadly distributed with respect to tree size. In warmer and wetter forests, aboveground biomass and woody productivity were more right skewed, with a long tail towards large trees. Small trees (1-10 cm diameter) contributed more to productivity and mortality than to biomass, highlighting the importance of including these trees in analyses of forest dynamics. Our findings provide an improved characterization of climate-driven forest differences in the size structure of aboveground biomass and dynamics of that biomass, as well as refined benchmarks for capturing climate influences in vegetation demographic models.
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Affiliation(s)
- Camille Piponiot
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Apartado Postal 0843-03092, Panama City, Panama
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA, 22630, USA
- UR Forests and Societies, Cirad, Université de Montpellier, Montpellier, 34000, France
| | - Kristina J Anderson-Teixeira
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Apartado Postal 0843-03092, Panama City, Panama
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA, 22630, USA
| | - Stuart J Davies
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Apartado Postal 0843-03092, Panama City, Panama
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Washington, DC, 20560, USA
- Department of Botany, National Museum of Natural History, Washington, DC, 20560, USA
| | - David Allen
- Department of Biology, Middlebury College, Middlebury, VT, 05753, USA
| | - Norman A Bourg
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA, 22630, USA
| | - David F R P Burslem
- School of Biological Sciences, University of Aberdeen, Aberdeen, AB24 3UU, UK
| | - Dairon Cárdenas
- Instituto Amazónico de Investigaciones Científicas Sinchi, Bogota, DC, Colombia
| | - Chia-Hao Chang-Yang
- Department of Biological Sciences, National Sun Yat-sen University, Kaohsiung City, 80424
| | - George Chuyong
- Department of Botany and Plant Physiology, University of Buea, Buea, Cameroon
| | - Susan Cordell
- Institute of Pacific Islands Forestry, USDA Forest Service, Hilo, HI, 96720, USA
| | | | - Álvaro Duque
- Departamento de Ciencias Forestales, Universidad Nacional de Colombia Sede Medellín, Medellín, Colombia
| | - Sisira Ediriweera
- Department of Science and Technology, Faculty of Applied Sciences, Uva Wellassa University, Badulla, 90000, Sri Lanka
| | - Corneille Ewango
- Faculty of Sciences, University of Kisangani, BP 2012, Kisangani, Democratic Republic of the Congo
| | - Zacky Ezedin
- Department of Plant & Microbial Biology, University of Minnesota, St Paul, MN, 55108, USA
| | - Jonah Filip
- Binatang Research Centre, Madang, Papua New Guinea
| | - Christian P Giardina
- Institute of Pacific Islands Forestry, USDA Forest Service, Hilo, HI, 96720, USA
| | - Robert Howe
- Department of Natural and Applied Sciences, University of Wisconsin-Green Bay, Green Bay, WI, 54311-7001, USA
| | - Chang-Fu Hsieh
- Institute of Ecology and Evolutionary Biology, National Taiwan University, Taipei, 10617
| | - Stephen P Hubbell
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Apartado Postal 0843-03092, Panama City, Panama
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | | | - Akira Itoh
- Graduate School of Science, Osaka City University, Osaka, 5588585, Japan
| | - David Janík
- Department of Forest Ecology, Silva Tarouca Research Institute, Brno, 602 00, Czech Republic
| | - David Kenfack
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Apartado Postal 0843-03092, Panama City, Panama
- Department of Botany, National Museum of Natural History, Washington, DC, 20560, USA
| | - Kamil Král
- Department of Forest Ecology, Silva Tarouca Research Institute, Brno, 602 00, Czech Republic
| | - James A Lutz
- Wildland Resources Department, Utah State University, Logan, UT, 84322, USA
| | - Jean-Remy Makana
- Faculty of Sciences, University of Kisangani, BP 2012, Kisangani, Democratic Republic of the Congo
| | - Sean M McMahon
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Apartado Postal 0843-03092, Panama City, Panama
- Forest Global Earth Observatory, Smithsonian Environmental Research Center, Edgewater, MD, 21037, USA
| | - William McShea
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA, 22630, USA
| | - Xiangcheng Mi
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093
| | - Mohizah Bt Mohamad
- Research Development and Innovation Division, Forest Department Sarawak, Bangunan Baitul Makmur 2, Medanraya, Petrajaya, Kuching, 93050, Malaysia
| | - Vojtěch Novotný
- Binatang Research Centre, Madang, Papua New Guinea
- Biology Centre, Academy of Sciences of the Czech Republic and Faculty of Science, University of South Bohemia, Ceske Budejovice, 37005, Czech Republic
| | - Michael J O'Brien
- Área de Biodiversidad y Conservación, Universidad Rey Juan Carlos, Móstoles, 28933, Spain
| | - Rebecca Ostertag
- Department of Biology, University of Hawaii, Hilo, HI, 96720, USA
| | - Geoffrey Parker
- Forest Ecology Group, Smithsonian Environmental Research Center, Edgewater, MD, 21037, USA
| | - Rolando Pérez
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Apartado Postal 0843-03092, Panama City, Panama
| | - Haibao Ren
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093
| | - Glen Reynolds
- The Royal Society SEARRP (UK/Malaysia), Danum Valley Field Centre, Lahad Datu, Sabah, Malaysia
| | - Mohamad Danial Md Sabri
- Forestry and Environment Division, Forest Research Institute Malaysia, Kepong, Selangor, 52109, Malaysia
| | - Lawren Sack
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Ankur Shringi
- Centre for Ecological Sciences, Indian Institute of Science, Bangalore, Karnataka, India
| | | | - Raman Sukumar
- Centre for Ecological Sciences and Divecha Centre for Climate Change, Indian Institute of Science, Bangalore, Karnataka, India
| | - I-Fang Sun
- Department of Natural Resources and Environmental Studies, National Dong Hwa University, Hualien, 974301
| | - Hebbalalu S Suresh
- Centre for Ecological Sciences and Divecha Centre for Climate Change, Indian Institute of Science, Bangalore, Karnataka, India
| | - Duncan W Thomas
- School of Biological Sciences, Washington State University, Vancouver, WA, 99164, USA
| | - Jill Thompson
- UK Centre for Ecology and Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0SB, UK
| | - Maria Uriarte
- Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY, 10027, USA
| | - John Vandermeer
- Department of Ecology and Evolutionary Biology and Herbarium, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yunquan Wang
- College of Chemistry and Life Sciences, Zhejiang Normal University, Jinhua, 321004
| | - Ian M Ware
- Institute of Pacific Islands Forestry, USDA Forest Service, Hilo, HI, 96720, USA
| | - George D Weiblen
- Department of Plant & Microbial Biology, University of Minnesota, St Paul, MN, 55108, USA
| | | | - Amy Wolf
- Department of Natural and Applied Sciences, University of Wisconsin-Green Bay, Green Bay, WI, 54311-7001, USA
| | - Tze Leong Yao
- Forestry and Environment Division, Forest Research Institute Malaysia, Kepong, Selangor, 52109, Malaysia
| | - Mingjian Yu
- College of Life Sciences, Zhejiang University, Hangzhou
| | - Zuoqiang Yuan
- CAS Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016
| | - Jess K Zimmerman
- Department of Environmental Sciences, University of Puerto Rico, San Juan, PR, USA
| | - Daniel Zuleta
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Apartado Postal 0843-03092, Panama City, Panama
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Washington, DC, 20560, USA
| | - Helene C Muller-Landau
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Apartado Postal 0843-03092, Panama City, Panama
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13
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Applying a Portable Backpack Lidar to Measure and Locate Trees in a Nature Forest Plot: Accuracy and Error Analyses. REMOTE SENSING 2022. [DOI: 10.3390/rs14081806] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation satellite system receiver has greater flexibility for tree inventory than terrestrial laser scanning, but it has never been used to measure and map forest structure in a large area (>101 hectares) with high tree density. In the present study, we used the LiBackpack DG50 backpack lidar system to obtain the point cloud data of a 10 ha plot of subtropical evergreen broadleaved forest, and applied these data to quantify errors and related factors in the diameter at breast height (DBH) measurements and positioning for more than 1900 individual trees. We found an average error of 4.19 cm in the DBH measurements obtained by lidar, compared with manual field measurements. The incompleteness of the tree stem point clouds was the main factor that caused the DBH measurement errors, and the field DBH measurements and density of the point clouds also had significant impacts. The average tree positioning error was 4.64 m, and it was significantly affected by the distance and route length from the measured trees to the data acquisition start position, whereas it was affected little by the habitat complexity and characteristics of tree stems. The tree positioning measurement error led to increases in the mean value and variability of paired-tree distance error as the sample plot scale increased. We corrected the errors based on the estimates of predictive models. After correction, the DBH measurement error decreased by 31.3%, the tree positioning error decreased by 44.3%, and the paired-tree distance error decreased by 56.3%. As the sample plot scale increased, the accumulated paired-tree distance error stabilized gradually.
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14
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Ribas Costa VA, Durand M, Robson TM, Porcar‐Castell A, Korpela I, Atherton J. Uncrewed aircraft system spherical photography for the vertical characterization of canopy structural traits. THE NEW PHYTOLOGIST 2022; 234:735-747. [PMID: 35090193 PMCID: PMC9303749 DOI: 10.1111/nph.17998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 12/19/2021] [Indexed: 06/14/2023]
Abstract
The plant area index (PAI) is a structural trait that succinctly parametrizes the foliage distribution of a canopy and is usually estimated using indirect optical techniques such as digital hemispherical photography. Critically, on-the-ground photographic measurements forgo the vertical variation of canopy structure which regulates the local light environment. Hence new approaches are sought for vertical sampling of traits. We present an uncrewed aircraft system (UAS) spherical photographic method to obtain structural traits throughout the depth of tree canopies. Our method explained 89% of the variation in PAI when compared with ground-based hemispherical photography. When comparing UAS vertical trait profiles with airborne laser scanning data, we found highest agreement in an open birch (Betula pendula/pubescens) canopy. Minor disagreement was found in dense spruce (Picea abies) stands, especially in the lower canopy. Our new method enables easy estimation of the vertical dimension of canopy structural traits in previously inaccessible spaces. The method is affordable and safe and therefore readily usable by plant scientists.
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Affiliation(s)
- Vicent Agustí Ribas Costa
- Optics of Photosynthesis LaboratoryInstitute for Atmospheric and Earth System Research (INAR)/Department of Forest SciencesViikki Plant Science Centre (ViPS)Faculty of Agriculture and ForestryUniversity of HelsinkiHelsinki00014Finland
| | - Maxime Durand
- Organismal and Evolutionary Biology (OEB)Viikki Plant Science Centre (ViPS)Faculty of Biological and Environmental SciencesUniversity of HelsinkiHelsinki00014Finland
| | - T. Matthew Robson
- Organismal and Evolutionary Biology (OEB)Viikki Plant Science Centre (ViPS)Faculty of Biological and Environmental SciencesUniversity of HelsinkiHelsinki00014Finland
| | - Albert Porcar‐Castell
- Optics of Photosynthesis LaboratoryInstitute for Atmospheric and Earth System Research (INAR)/Department of Forest SciencesViikki Plant Science Centre (ViPS)Faculty of Agriculture and ForestryUniversity of HelsinkiHelsinki00014Finland
| | - Ilkka Korpela
- Department of Forest SciencesFaculty of Agriculture and ForestryUniversity of HelsinkiHelsinki00014Finland
| | - Jon Atherton
- Optics of Photosynthesis LaboratoryInstitute for Atmospheric and Earth System Research (INAR)/Department of Forest SciencesViikki Plant Science Centre (ViPS)Faculty of Agriculture and ForestryUniversity of HelsinkiHelsinki00014Finland
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15
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Norby RJ, Warren JM, Iversen CM, Childs J, Jawdy SS, Walker AP. Forest stand and canopy development unaltered by 12 years of CO2 enrichment. TREE PHYSIOLOGY 2022; 42:428-440. [PMID: 34387351 PMCID: PMC8919409 DOI: 10.1093/treephys/tpab107] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 08/05/2021] [Indexed: 06/13/2023]
Abstract
Canopy structure-the size and distribution of tree crowns and the spatial and temporal distribution of leaves within them-exerts dominant control over primary productivity, transpiration and energy exchange. Stand structure-the spatial arrangement of trees in the forest (height, basal area and spacing)-has a strong influence on forest growth, allocation and resource use. Forest response to elevated atmospheric CO2 is likely to be dependent on the canopy and stand structure. Here, we investigated elevated CO2 effects on the forest structure of a Liquidambar styraciflua L. stand in a free-air CO2 enrichment experiment, considering leaves, tree crowns, forest canopy and stand structure. During the 12-year experiment, the trees increased in height by 5 m and basal area increased by 37%. Basal area distribution among trees shifted from a relatively narrow distribution to a much broader one, but there was little evidence of a CO2 effect on height growth or basal area distribution. The differentiation into crown classes over time led to an increase in the number of unproductive intermediate and suppressed trees and to a greater concentration of stand basal area in the largest trees. A whole-tree harvest at the end of the experiment permitted detailed analysis of canopy structure. There was little effect of CO2 enrichment on the relative leaf area distribution within tree crowns and there was little change from 1998 to 2009. Leaf characteristics (leaf mass per unit area and nitrogen content) varied with crown depth; any effects of elevated CO2 were much smaller than the variation within the crown and were consistent throughout the crown. In this young, even-aged, monoculture plantation forest, there was little evidence that elevated CO2 accelerated tree and stand development, and there were remarkably small changes in canopy structure. Questions remain as to whether a more diverse, mixed species forest would respond similarly.
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Affiliation(s)
- Richard J Norby
- Department of Ecology & Evolutionary Biology, University of Tennessee, Knoxville, TN 37996, USA
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Jeffrey M Warren
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Colleen M Iversen
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Joanne Childs
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Sara S Jawdy
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Anthony P Walker
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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16
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Puletti N, Grotti M, Masini A, Bracci A, Ferrara C. Enhancing wall-to-wall forest structure mapping through detailed co-registration of airborne and terrestrial laser scanning data in Mediterranean forests. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2021.101497] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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17
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Nunes MH, Camargo JLC, Vincent G, Calders K, Oliveira RS, Huete A, Mendes de Moura Y, Nelson B, Smith MN, Stark SC, Maeda EE. Forest fragmentation impacts the seasonality of Amazonian evergreen canopies. Nat Commun 2022; 13:917. [PMID: 35177619 PMCID: PMC8854568 DOI: 10.1038/s41467-022-28490-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/27/2022] [Indexed: 11/09/2022] Open
Abstract
Predictions of the magnitude and timing of leaf phenology in Amazonian forests remain highly controversial. Here, we use terrestrial LiDAR surveys every two weeks spanning wet and dry seasons in Central Amazonia to show that plant phenology varies strongly across vertical strata in old-growth forests, but is sensitive to disturbances arising from forest fragmentation. In combination with continuous microclimate measurements, we find that when maximum daily temperatures reached 35 °C in the latter part of the dry season, the upper canopy of large trees in undisturbed forests lost plant material. In contrast, the understory greened up with increased light availability driven by the upper canopy loss, alongside increases in solar radiation, even during periods of drier soil and atmospheric conditions. However, persistently high temperatures in forest edges exacerbated the upper canopy losses of large trees throughout the dry season, whereas the understory in these light-rich environments was less dependent on the altered upper canopy structure. Our findings reveal a strong influence of edge effects on phenological controls in wet forests of Central Amazonia.
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Affiliation(s)
- Matheus Henrique Nunes
- Department of Geosciences and Geography, University of Helsinki, Helsinki, 00014, Finland.
| | - José Luís Campana Camargo
- Biological Dynamics of Forest Fragment Project, National Institute for Amazonian Research, Manaus, AM, 69067-375, Brazil
| | - Grégoire Vincent
- AMAP, Univ Montpellier, IRD, CIRAD, CNRS, INRAE, Montpellier, France
| | - Kim Calders
- CAVElab-Computational and Applied Vegetation Ecology, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Rafael S Oliveira
- Department of Plant Biology, Institute of Biology, University of Campinas, Campinas, Brazil
| | - Alfredo Huete
- School of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Yhasmin Mendes de Moura
- Institute of Geography and Geoecology, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131, Karlsruhe, Germany
- Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, Leicester, LE17RH, UK
| | - Bruce Nelson
- National Institute of Amazonian Research, Manaus, Brazil
| | - Marielle N Smith
- Department of Forestry, Michigan State University, East Lansing, MI, USA
| | - Scott C Stark
- Department of Forestry, Michigan State University, East Lansing, MI, USA
| | - Eduardo Eiji Maeda
- Department of Geosciences and Geography, University of Helsinki, Helsinki, 00014, Finland
- Area of Ecology and Biodiversity, School of Biological Sciences, Faculty of Science, The University of Hong Kong, Hong Kong, Hong Kong SAR
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18
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Wilkes P, Shenkin A, Disney M, Malhi Y, Bentley LP, Vicari MB. Terrestrial laser scanning to reconstruct branch architecture from harvested branches. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13709] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Phil Wilkes
- Department of Geography University College London London UK
- NERC National Centre for Earth Observation Leicester UK
| | - Alexander Shenkin
- Environmental Change Institute School of Geography and Environment University of Oxford Oxford UK
| | - Mathias Disney
- Department of Geography University College London London UK
- NERC National Centre for Earth Observation Leicester UK
| | - Yadvinder Malhi
- Environmental Change Institute School of Geography and Environment University of Oxford Oxford UK
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Stovall AEL, Masters B, Fatoyinbo L, Yang X. TLSLeAF: automatic leaf angle estimates from single-scan terrestrial laser scanning. THE NEW PHYTOLOGIST 2021; 232:1876-1892. [PMID: 34110621 DOI: 10.1111/nph.17548] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/04/2021] [Indexed: 06/12/2023]
Abstract
Leaf angle distribution (LAD) in forest canopies affects estimates of leaf area, light interception, and global-scale photosynthesis, but is often simplified to a single theoretical value. Here, we present TLSLeAF (Terrestrial Laser Scanning Leaf Angle Function), an automated open-source method of deriving LADs from terrestrial laser scanning. TLSLeAF produces canopy-scale leaf angle and LADs by relying on gridded laser scanning data. The approach increases processing speed, improves angle estimates, and requires minimal user input. Key features are automation, leaf-wood classification, beta parameter output, and implementation in R to increase accessibility for the ecology community. TLSLeAF precisely estimates leaf angle with minimal distance effects on angular estimates while rapidly producing LADs on a consumer-grade machine. We challenge the popular spherical LAD assumption, showing sensitivity to ecosystem type in plant area index and foliage profile estimates that translate to c. 25% and c. 11% increases in canopy net photosynthesis (c. 25%) and solar-induced chlorophyll fluorescence (c. 11%). TLSLeAF can now be applied to the vast catalog of laser scanning data already available from ecosystems around the globe. The ease of use will enable widespread adoption of the method outside of remote-sensing experts, allowing greater accessibility for addressing ecological hypotheses and large-scale ecosystem modeling efforts.
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Affiliation(s)
- Atticus E L Stovall
- Biospheric Sciences Lab, NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
- Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA
- Department of Environmental Sciences, University of Virginia, Charlottesville, VA, 22903, USA
| | - Benjamin Masters
- Department of Environmental Sciences, University of Virginia, Charlottesville, VA, 22903, USA
| | - Lola Fatoyinbo
- Biospheric Sciences Lab, NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
| | - Xi Yang
- Department of Environmental Sciences, University of Virginia, Charlottesville, VA, 22903, USA
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20
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O’Sullivan H, Raumonen P, Kaitaniemi P, Perttunen J, Sievänen R. Integrating terrestrial laser scanning with functional-structural plant models to investigate ecological and evolutionary processes of forest communities. ANNALS OF BOTANY 2021; 128:663-684. [PMID: 34610091 PMCID: PMC8557364 DOI: 10.1093/aob/mcab120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/13/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Woody plants (trees and shrubs) play an important role in terrestrial ecosystems, but their size and longevity make them difficult subjects for traditional experiments. In the last 20 years functional-structural plant models (FSPMs) have evolved: they consider the interplay between plant modular structure, the immediate environment and internal functioning. However, computational constraints and data deficiency have long been limiting factors in a broader application of FSPMs, particularly at the scale of forest communities. Recently, terrestrial laser scanning (TLS), has emerged as an invaluable tool for capturing the 3-D structure of forest communities, thus opening up exciting opportunities to explore and predict forest dynamics with FSPMs. SCOPE The potential synergies between TLS-derived data and FSPMs have yet to be fully explored. Here, we summarize recent developments in FSPM and TLS research, with a specific focus on woody plants. We then evaluate the emerging opportunities for applying FSPMs in an ecological and evolutionary context, in light of TLS-derived data, with particular consideration of the challenges posed by scaling up from individual trees to whole forests. Finally, we propose guidelines for incorporating TLS data into the FSPM workflow to encourage overlap of practice amongst researchers. CONCLUSIONS We conclude that TLS is a feasible tool to help shift FSPMs from an individual-level modelling technique to a community-level one. The ability to scan multiple trees, of multiple species, in a short amount of time, is paramount to gathering the detailed structural information required for parameterizing FSPMs for forest communities. Conventional techniques, such as repeated manual forest surveys, have their limitations in explaining the driving mechanisms behind observed patterns in 3-D forest structure and dynamics. Therefore, other techniques are valuable to explore how forests might respond to environmental change. A robust synthesis between TLS and FSPMs provides the opportunity to virtually explore the spatial and temporal dynamics of forest communities.
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Affiliation(s)
- Hannah O’Sullivan
- Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, SL5 7PY, UK
- Royal Botanic Gardens, Kew, Richmond, UK
| | - Pasi Raumonen
- Mathematics, Tampere University, Korkeakoulunkatu 7, FI-33720 Tampere, Finland
| | - Pekka Kaitaniemi
- Hyytiälä Forestry Field Station, Faculty of Agriculture and Forestry, University of Helsinki, Hyytiäläntie 124, FI-35500 Korkeakoski, Finland
| | - Jari Perttunen
- Natural Resources Institute Finland, Latokartanontie 9, 00790 Helsinki, Finland
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21
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Disney M. How can we know what we don't know? A Commentary on: Sampling forests with terrestrial laser scanning. ANNALS OF BOTANY 2021; 128:685-688. [PMID: 34564716 PMCID: PMC8557368 DOI: 10.1093/aob/mcab119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
This article comments on: Peter B. Boucher, Ian Paynter, David A. Orwig Ilan Valencius and Crystal Schaaf, Sampling forests with terrestrial laser scanning, Annals of Botany, Volume 128, Issue 6, 2 November 2021, Pages 685–687 https://doi.org/10.1093/aob/mcab073
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Affiliation(s)
- Mathias Disney
- UCL Geography, Gower Street, London WC1E 6BT, UK
- Corresponding author details: Mathias Disney,
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22
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Cocozza C, Traversi ML, Giovannelli A. Tree Growth Conditions Are Demanded When Optimal, Are Unwanted When Limited, but When Are They Suboptimal? PLANTS 2021; 10:plants10091943. [PMID: 34579475 PMCID: PMC8467812 DOI: 10.3390/plants10091943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 09/14/2021] [Accepted: 09/16/2021] [Indexed: 12/26/2022]
Abstract
The recent climate projections predict that the intensity and frequency of extreme events will increase as a result of overall increasing mean temperature and reduced precipitations in the temperate regions of the Northern Hemisphere. How these changes will influence the harshness of the environment and the performances of trees growing under natural conditions remains an open question. In this commentary article, we would like to look at the concept of suboptimal growth conditions, widening its application from the traditional in vitro manipulation to trees growing in open air, addressing the main limitations and strengths of the upscaling results from cell to tree. We believe that the traditional single dose–effect approach is not suitable to explain the complex interactions between genotype and environment, occurring in open field or forest stands, where the intensity and frequency of the events are uncontrolled and unpredictable. As forests provide a wide range of ecosystem services, new parameters should be considered in the definition of the response thresholds in addition to growth. Thus, within this Special Issue, we stimulate the discussion over the development of new approaches and technologies that are able to define suitable threshold responses of trees under suboptimal natural conditions, with the aim to furnish new insights on the acclimation and adaptation processes in woody species under global change.
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Affiliation(s)
- Claudia Cocozza
- Department of Agriculture, Food, Environment and Forestry, Via San Bonaventura 13, I-50145 Florence, Italy;
| | - Maria Laura Traversi
- CNR—Institute of Research on Terrestrial Ecosystems, Via Madonna del Piano 10, I-50019 Sesto Fiorentino, Italy;
| | - Alessio Giovannelli
- CNR—Institute of Research on Terrestrial Ecosystems, Via Madonna del Piano 10, I-50019 Sesto Fiorentino, Italy;
- Correspondence:
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23
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Reprint of: Functional-structural plant models to boost understanding of complementarity in light capture and use in mixed-species forests. Basic Appl Ecol 2021. [DOI: 10.1016/j.baae.2021.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Zheng S, Webber BL, Didham RK, Chen C, Yu M. Disentangling biotic and abiotic drivers of intraspecific trait variation in woody plant seedlings at forest edges. Ecol Evol 2021; 11:9728-9740. [PMID: 34306658 PMCID: PMC8293732 DOI: 10.1002/ece3.7799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 05/23/2021] [Accepted: 05/27/2021] [Indexed: 11/08/2022] Open
Abstract
In fragmented forests, edge effects can drive intraspecific variation in seedling performance that influences forest regeneration and plant composition. However, few studies have attempted to disentangle the relative biotic and abiotic drivers of intraspecific variation in seedling performance. In this study, we carried out a seedling transplant experiment with a factorial experimental design on three land-bridge islands in the Thousand Island Lake, China, using four common native woody plant species. At different distances from the forest edge (2, 8, 32, 128 m), we transplanted four seedlings of each species into each of three cages: full-cage, for herbivore exclusion; half-cage, that allowed herbivore access but controlled for caging artifacts; and no-cage control. In the 576 cages, we recorded branch architecture, leaf traits, and seedling survival for each seedling before and after the experimental treatment. Overall, after one full growing season, edge-induced abiotic drivers and varied herbivory pressure led to intraspecific variation in seedling performance, including trade-offs in seedling architecture and resource-use strategies. However, responses varied across species with different life-history strategies and depended on the driver in question, such that the abiotic and biotic effects were additive across species, rather than interactive. Edge-induced abiotic variation modified seedling architecture of a shade-tolerant species, leading to more vertical rather than lateral growth at edges. Meanwhile, increased herbivory pressure resulted in a shift toward lower dry matter investment in leaves of a light-demanding species. Our results suggest that edge effects can drive rapid directional shifts in the performance and intraspecific traits of some woody plants from early ontogenetic stages, but most species in this study showed negligible phenotypic responses to edge effects. Moreover, species-specific responses suggest the importance of interspecific differences modulating the degree of trait plasticity, implying the need to incorporate individual-level responses when understanding the impact of forest fragmentation on plant communities.
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Affiliation(s)
- Shilu Zheng
- School of Biological SciencesThe University of Western AustraliaCrawleyWAAustralia
- Centre for Environment and Life SciencesCSIRO Health & BiosecurityFloreatWAAustralia
| | - Bruce L. Webber
- School of Biological SciencesThe University of Western AustraliaCrawleyWAAustralia
- Centre for Environment and Life SciencesCSIRO Health & BiosecurityFloreatWAAustralia
- Western Australian Biodiversity Science InstitutePerthWAAustralia
| | - Raphael K. Didham
- School of Biological SciencesThe University of Western AustraliaCrawleyWAAustralia
- Centre for Environment and Life SciencesCSIRO Health & BiosecurityFloreatWAAustralia
| | - Chun Chen
- College of Life SciencesZhejiang UniversityHangzhouChina
| | - Mingjian Yu
- College of Life SciencesZhejiang UniversityHangzhouChina
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Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare? REMOTE SENSING 2021. [DOI: 10.3390/rs13122297] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.
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Maréchaux I, Langerwisch F, Huth A, Bugmann H, Morin X, Reyer CP, Seidl R, Collalti A, Dantas de Paula M, Fischer R, Gutsch M, Lexer MJ, Lischke H, Rammig A, Rödig E, Sakschewski B, Taubert F, Thonicke K, Vacchiano G, Bohn FJ. Tackling unresolved questions in forest ecology: The past and future role of simulation models. Ecol Evol 2021; 11:3746-3770. [PMID: 33976773 PMCID: PMC8093733 DOI: 10.1002/ece3.7391] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 02/04/2021] [Accepted: 02/20/2021] [Indexed: 12/13/2022] Open
Abstract
Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio-temporal scales unreachable by most empirical investigations.We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems.Using three widely applied but contrasting approaches - species distribution models, individual-based forest models, and dynamic global vegetation models - as examples, we show how scientific and technical advances have led models to transgress their initial objectives and limitations. We provide an overview of recent model applications on current important ecological topics and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.Synthesis. This overview shows that forest models, due to their complementarity and mutual enrichment, represent an invaluable toolkit to address a wide range of fundamental and applied ecological questions, hence fostering a deeper understanding of forest dynamics in the context of global change.
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Affiliation(s)
| | - Fanny Langerwisch
- Department of Ecology and Environmental SciencesPalacký University OlomoucOlomoucCzech Republic
- Department of Water Resources and Environmental ModelingCzech University of Life SciencesPragueCzech Republic
| | - Andreas Huth
- Helmholtz Centre for Environmental Research ‐ UFZLeipzigGermany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
- Institute of Environmental Systems ResearchOsnabrück UniversityOsnabrückGermany
| | - Harald Bugmann
- Forest EcologyInstitute of Terrestrial EcosystemsETH ZürichZurichSwitzerland
| | - Xavier Morin
- EPHECEFECNRSUniv MontpellierUniv Paul Valéry MontpellierIRDMontpellierFrance
| | - Christopher P.O. Reyer
- Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationPotsdamGermany
| | - Rupert Seidl
- Institute of SilvicultureUniversity of Natural Resources and Life Sciences (BOKU)ViennaAustria
- TUM School of Life SciencesTechnical University of MunichFreisingGermany
| | - Alessio Collalti
- Forest Modelling LabInstitute for Agriculture and Forestry Systems in the MediterraneanNational Research Council of Italy (CNR‐ISAFOM)Perugia (PG)Italy
- Department of Innovation in Biological, Agro‐food and Forest SystemsUniversity of TusciaViterboItaly
| | | | - Rico Fischer
- Helmholtz Centre for Environmental Research ‐ UFZLeipzigGermany
| | - Martin Gutsch
- Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationPotsdamGermany
| | | | - Heike Lischke
- Dynamic MacroecologyLand Change ScienceSwiss Federal Institute for Forest, Snow and Landscape Research WSLBirmensdorfSwitzerland
| | - Anja Rammig
- TUM School of Life SciencesTechnical University of MunichFreisingGermany
| | - Edna Rödig
- Helmholtz Centre for Environmental Research ‐ UFZLeipzigGermany
| | - Boris Sakschewski
- Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationPotsdamGermany
| | | | - Kirsten Thonicke
- Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationPotsdamGermany
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27
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Seidel D, Annighöfer P, Thielman A, Seifert QE, Thauer JH, Glatthorn J, Ehbrecht M, Kneib T, Ammer C. Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning. FRONTIERS IN PLANT SCIENCE 2021; 12:635440. [PMID: 33643364 PMCID: PMC7902704 DOI: 10.3389/fpls.2021.635440] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/19/2021] [Indexed: 06/12/2023]
Abstract
Automated species classification from 3D point clouds is still a challenge. It is, however, an important task for laser scanning-based forest inventory, ecosystem models, and to support forest management. Here, we tested the performance of an image classification approach based on convolutional neural networks (CNNs) with the aim to classify 3D point clouds of seven tree species based on 2D representation in a computationally efficient way. We were particularly interested in how the approach would perform with artificially increased training data size based on image augmentation techniques. Our approach yielded a high classification accuracy (86%) and the confusion matrix revealed that despite rather small sample sizes of the training data for some tree species, classification accuracy was high. We could partly relate this to the successful application of the image augmentation technique, improving our result by 6% in total and 13, 14, and 24% for ash, oak and pine, respectively. The introduced approach is hence not only applicable to small-sized datasets, it is also computationally effective since it relies on 2D instead of 3D data to be processed in the CNN. Our approach was faster and more accurate when compared to the point cloud-based "PointNet" approach.
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Affiliation(s)
- Dominik Seidel
- Faculty of Forest Sciences, Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, Germany
| | - Peter Annighöfer
- Forest and Agroforest Systems, Technical University of Munich, Freising, Germany
| | - Anton Thielman
- Campus Institute Data Science and Chairs of Statistics and Econometries, Göttingen, Germany
| | - Quentin Edward Seifert
- Campus Institute Data Science and Chairs of Statistics and Econometries, Göttingen, Germany
| | - Jan-Henrik Thauer
- Campus Institute Data Science and Chairs of Statistics and Econometries, Göttingen, Germany
| | - Jonas Glatthorn
- Faculty of Forest Sciences, Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, Germany
| | - Martin Ehbrecht
- Faculty of Forest Sciences, Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, Germany
| | - Thomas Kneib
- Campus Institute Data Science and Chairs of Statistics and Econometries, Göttingen, Germany
| | - Christian Ammer
- Faculty of Forest Sciences, Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, Germany
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28
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Burt A, Boni Vicari M, da Costa ACL, Coughlin I, Meir P, Rowland L, Disney M. New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. ROYAL SOCIETY OPEN SCIENCE 2021; 8:201458. [PMID: 33972856 PMCID: PMC8074798 DOI: 10.1098/rsos.201458] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 01/18/2021] [Indexed: 06/12/2023]
Abstract
A large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly owing to the lack of measurements. To date, accessible peer-reviewed data are available for just 10 large tropical trees in the Amazon that have been harvested and directly measured entirely via weighing. Here, we harvested four large tropical rainforest trees (stem diameter: 0.6-1.2 m, height: 30-46 m, AGB: 3960-18 584 kg) in intact old-growth forest in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We first present rare ecological insights provided by these data, including unsystematic intra-tree variations in density, with both height and radius. We also found the majority of AGB was usually found in the crown, but varied from 42 to 62%. We then compare non-destructive approaches for estimating the AGB of these trees, using both classical allometry and new lidar-based methods. Terrestrial lidar point clouds were collected pre-harvest, on which we fitted cylinders to model woody structure, enabling retrieval of volume-derived AGB. Estimates from this approach were more accurate than allometric counterparts (mean tree-scale relative error: 3% versus 15%), and error decreased when up-scaling to the cumulative AGB of the four trees (1% versus 15%). Furthermore, while allometric error increased fourfold with tree size over the diameter range, lidar error remained constant. This suggests error in these lidar-derived estimates is random and additive. Were these results transferable across forest scenes, terrestrial lidar methods would reduce uncertainty in stand-scale AGB estimates, and therefore advance our understanding of the role of tropical forests in the global carbon cycle.
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Affiliation(s)
- Andrew Burt
- Department of Geography, University College London, London, UK
| | | | | | - Ingrid Coughlin
- Research School of Biology, Australian National University, Canberra, Australia
| | - Patrick Meir
- Research School of Biology, Australian National University, Canberra, Australia
- School of GeoSciences, University of Edinburgh, Edinburgh, UK
| | - Lucy Rowland
- College of Life and Environmental Sciences, University of Exeter, Exeter, UK
| | - Mathias Disney
- Department of Geography, University College London, London, UK
- NERC National Centre for Earth Observation (NCEO), Leicester, UK
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Functional-structural plant models to boost understanding of complementarity in light capture and use in mixed-species forests. Basic Appl Ecol 2020. [DOI: 10.1016/j.baae.2020.09.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Disney M, Burt A, Wilkes P, Armston J, Duncanson L. New 3D measurements of large redwood trees for biomass and structure. Sci Rep 2020; 10:16721. [PMID: 33060622 PMCID: PMC7566452 DOI: 10.1038/s41598-020-73733-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 09/21/2020] [Indexed: 11/13/2022] Open
Abstract
Large trees are disproportionately important in terms of their above ground biomass (AGB) and carbon storage, as well as their wider impact on ecosystem structure. They are also very hard to measure and so tend to be underrepresented in measurements and models of AGB. We show the first detailed 3D terrestrial laser scanning (TLS) estimates of the volume and AGB of large coastal redwood Sequoia sempervirens trees from three sites in Northern California, representing some of the highest biomass ecosystems on Earth. Our TLS estimates agree to within 2% AGB with a species-specific model based on detailed manual crown mapping of 3D tree structure. However TLS-derived AGB was more than 30% higher compared to widely-used general (non species-specific) allometries. We derive an allometry from TLS that spans a much greater range of tree size than previous models and so is potentially better-suited for use with new Earth Observation data for these exceptionally high biomass areas. We suggest that where possible, TLS and crown mapping should be used to provide complementary, independent 3D structure measurements of these very large trees.
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Affiliation(s)
| | - Andrew Burt
- UCL Geography, Gower Street, London, WC1E 6BT, UK
| | - Phil Wilkes
- UCL Geography, Gower Street, London, WC1E 6BT, UK
- NERC National Centre for Earth Observation (NCEO), UCL, Gower Street, London, WC1E 6BT, UK
| | - John Armston
- Department of Geographical Sciences, University of Maryland, College Park, 2181 Lefrak Hall, College Park, MD, 20742, USA
| | - Laura Duncanson
- Department of Geographical Sciences, University of Maryland, College Park, 2181 Lefrak Hall, College Park, MD, 20742, USA
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Martin‐Ducup O, Ploton P, Barbier N, Momo Takoudjou S, Mofack G, Kamdem NG, Fourcaud T, Sonké B, Couteron P, Pélissier R. Terrestrial laser scanning reveals convergence of tree architecture with increasingly dominant crown canopy position. Funct Ecol 2020. [DOI: 10.1111/1365-2435.13678] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
| | - Pierre Ploton
- AMAP, IRDCNRSCIRADINRAUniversity of Montpellier Montpellier France
| | - Nicolas Barbier
- AMAP, IRDCNRSCIRADINRAUniversity of Montpellier Montpellier France
| | - Stéphane Momo Takoudjou
- Plant Systematic and Ecology Laboratory Higher Teacher's Training College University of Yaoundé I Yaoundé Cameroon
| | - Gislain Mofack
- Plant Systematic and Ecology Laboratory Higher Teacher's Training College University of Yaoundé I Yaoundé Cameroon
| | - Narcisse Guy Kamdem
- Plant Systematic and Ecology Laboratory Higher Teacher's Training College University of Yaoundé I Yaoundé Cameroon
| | - Thierry Fourcaud
- AMAP, IRDCNRSCIRADINRAUniversity of Montpellier Montpellier France
| | - Bonaventure Sonké
- Plant Systematic and Ecology Laboratory Higher Teacher's Training College University of Yaoundé I Yaoundé Cameroon
| | - Pierre Couteron
- AMAP, IRDCNRSCIRADINRAUniversity of Montpellier Montpellier France
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Drought-modulated allometric patterns of trees in semi-arid forests. Commun Biol 2020; 3:405. [PMID: 32733028 PMCID: PMC7393108 DOI: 10.1038/s42003-020-01144-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 07/09/2020] [Indexed: 11/09/2022] Open
Abstract
Tree allometry in semi-arid forests is characterized by short height but large canopy. This pattern may be important for maintaining water-use efficiency and carbon sequestration simultaneously, but still lacks quantification. Here we use terrestrial laser scanning to quantify allometry variations of Quercus mongolica in semi-arid forests. With tree height (Height) declining, canopy area (CA) decreases with substantial variations. The theoretical CA-Height relationship in dynamic global vegetation models (DGVMs) matches only the 5th percentile of our results because of CA underestimation and Height overestimation by breast height diameter (DBH). Water supply determines Height variation (P = 0.000) but not CA (P = 0.2 in partial correlation). The decoupled functions of stem, hydraulic conductance and leaf spatial arrangement, may explain the inconsistency, which may further ensure hydraulic safety and carbon assimilation, avoiding forest dieback. Works on tree allometry pattern and determinant will effectively supply tree drought tolerance studying and support DGVM improvements.
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Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model. REMOTE SENSING 2020. [DOI: 10.3390/rs12152426] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Traditional methods for individual tree-crown (ITC) detection (image classification, segmentation, template matching, etc.) applied to very high-resolution remote sensing imagery have been shown to struggle in disparate landscape types or image resolutions due to scale problems and information complexity. Deep learning promised to overcome these shortcomings due to its superior performance and versatility, proven with reported detection rates of ~90%. However, such models still find their limits in transferability across study areas, because of different tree conditions (e.g., isolated trees vs. compact forests) and/or resolutions of the input data. This study introduces a highly replicable deep learning ensemble design for ITC detection and species classification based on the established single shot detector (SSD) model. The ensemble model design is based on varying the input data for the SSD models, coupled with a voting strategy for the output predictions. Very high-resolution unmanned aerial vehicles (UAV), aerial remote sensing imagery and elevation data are used in different combinations to test the performance of the ensemble models in three study sites with highly contrasting spatial patterns. The results show that ensemble models perform better than any single SSD model, regardless of the local tree conditions or image resolution. The detection performance and the accuracy rates improved by 3–18% with only as few as two participant single models, regardless of the study site. However, when more than two models were included, the performance of the ensemble models only improved slightly and even dropped.
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Assessing the Performance of ICESat-2/ATLAS Multi-Channel Photon Data for Estimating Ground Topography in Forested Terrain. REMOTE SENSING 2020. [DOI: 10.3390/rs12132084] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
As a continuation of Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1), the ICESat-2/Advanced Topographic Laser Altimeter System (ATLAS) employs a micro-pulse multi-beam photon counting approach to produce photon data for measuring global terrain. Few studies have assessed the accuracy of different ATLAS channels in retrieving ground topography in forested terrain. This study aims to assess the accuracy of measuring ground topography in forested terrain using different ATLAS channels and the correlation between laser intensity parameters, laser pointing angle parameters, and elevation error. The accuracy of ground topography measured by the ATLAS footprints is evaluated by comparing the derived Digital Terrain Model (DTM) from the ATL03 (Global Geolocated Photon Data) and ATL08 (Land and Vegetation Height) products with that from the airborne Light Detection And Ranging (LiDAR). Results show that the ATLAS product performed well in the study area at all laser intensities and laser pointing angles, and correlations were found between the ATLAS DTM and airborne LiDAR DTM (coefficient of determination––R2 = 1.00, root mean squared error––RMSE = 0.75 m). Considering different laser intensities, there is a significant correlation between the tx_pulse_energy parameter and elevation error. With different laser pointing angles, there is no significant correlation between the tx_pulse_skew_est, tx_pulse_width_lower, tx_pulse_width_upper parameters and the elevation error.
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A New Quantitative Approach to Tree Attributes Estimation Based on LiDAR Point Clouds. REMOTE SENSING 2020. [DOI: 10.3390/rs12111779] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Tree-level information can be estimated based on light detection and ranging (LiDAR) point clouds. We propose to develop a quantitative structural model based on terrestrial laser scanning (TLS) point clouds to automatically and accurately estimate tree attributes and to detect real trees for the first time. This model is suitable for forest research where branches are involved in the calculation. First, the Adtree method was used to approximate the geometry of the tree stem and branches by fitting a series of cylinders. Trees were represented as a broad set of cylinders. Then, the end of the stem or all branches were closed. The tree model changed from a cylinder to a closed convex hull polyhedron, which was to reconstruct a 3D model of the tree. Finally, to extract effective tree attributes from the reconstructed 3D model, a convex hull polyhedron calculation method based on the tree model was defined. This calculation method can be used to extract wood (including tree stem and branches) volume, diameter at breast height (DBH) and tree height. To verify the accuracy of tree attributes extracted from the model, the tree models of 153 Chinese scholartrees from TLS data were reconstructed and the tree volume, DBH and tree height were extracted from the model. The experimental results show that the DBH and tree height extracted based on this model are in better consistency with the reference value based on field survey data. The bias, RMSE and R2 of DBH were 0.38 cm, 1.28 cm and 0.92, respectively. The bias, RMSE and R2 of tree height were −0.76 m, 1.21 m and 0.93, respectively. The tree volume extracted from the model is in better consistency with the reference value. The bias, root mean square error (RMSE) and determination coefficient (R2) of tree volume were −0.01236 m3, 0.03498 m3 and 0.96, respectively. This study provides a new model for nondestructive estimation of tree volume, above-ground biomass (AGB) or carbon stock based on LiDAR data.
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36
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Meir P, Mencuccini M, Coughlin SI. Respiration in wood: integrating across tissues, functions and scales. THE NEW PHYTOLOGIST 2020; 225:1824-1827. [PMID: 31872466 DOI: 10.1111/nph.16354] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Affiliation(s)
- Patrick Meir
- Research School of Biology, Australian National University, Canberra, ACT, 2601, Australia
- School of Geosciences, University of Edinburgh, Edinburgh, EH9 3FF, UK
| | - Maurizio Mencuccini
- ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain
- CREAF, Universidad Autonoma de Barcelona, Cerdanyola del Valles, 08193, Barcelona, Spain
| | - S Ingrid Coughlin
- Research School of Biology, Australian National University, Canberra, ACT, 2601, Australia
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Wang D, Momo Takoudjou S, Casella E. LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR. Methods Ecol Evol 2020. [DOI: 10.1111/2041-210x.13342] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Di Wang
- Department of Built Environment Aalto University Aalto Finland
| | - Stéphane Momo Takoudjou
- Institut de Recherche pour le Développement (IRD) URM AMAP Montpellier cedex 5 France
- Plant Systematic and Ecology Laboratory Higher Teacher's Training College University of Yaoundé I Yaoundé Cameroon
| | - Eric Casella
- Centre for Sustainable Forestry and Climate Change Forest Research Farnham UK
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38
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Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field. REMOTE SENSING 2020. [DOI: 10.3390/rs12020269] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R2 > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding.
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39
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Schimel D, Schneider FD. Flux towers in the sky: global ecology from space. THE NEW PHYTOLOGIST 2019; 224:570-584. [PMID: 31112309 DOI: 10.1111/nph.15934] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Accepted: 04/29/2019] [Indexed: 05/25/2023]
Abstract
Global ecology - the study of the interactions among the Earth's ecosystems, land, atmosphere and oceans - depends crucially on global observations: this paper focuses on space-based observations of global terrestrial ecosystems. Early global ecology relied on an extrapolation of detailed site-level observations, using models of increasing complexity. Modern global ecology has been enabled largely by vegetation indices (greenness) from operational space-based imagery but current capabilities greatly expand scientific possibilities. New observations from spacecraft in orbit allowed an estimation of gross carbon fluxes, photosynthesis, biomass burning, evapotranspiration and biomass, to create virtual eddy covariance sites in the sky. Planned missions will reveal the dimensions of the diversity of life itself. These observations will improve our understanding of the global productivity and carbon storage, land use, carbon cycle-climate feedback, diversity-productivity relationships and enable improved climate forecasts. Advances in remote sensing challenge ecologists to relate information organised by biome and species to new data arrayed by pixels and develop theory to address previously unobserved scales.
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Affiliation(s)
- David Schimel
- Jet Propulsion Lab, California Institute of Technology, Pasadena, CA, 91101, USA
| | - Fabian D Schneider
- Jet Propulsion Lab, California Institute of Technology, Pasadena, CA, 91101, USA
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40
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Paulus S. Measuring crops in 3D: using geometry for plant phenotyping. PLANT METHODS 2019; 15:103. [PMID: 31497064 PMCID: PMC6719375 DOI: 10.1186/s13007-019-0490-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 08/27/2019] [Indexed: 05/22/2023]
Abstract
Using 3D sensing for plant phenotyping has risen within the last years. This review provides an overview on 3D traits for the demands of plant phenotyping considering different measuring techniques, derived traits and use-cases of biological applications. A comparison between a high resolution 3D measuring device and an established measuring tool, the leaf meter, is shown to categorize the possible measurement accuracy. Furthermore, different measuring techniques such as laser triangulation, structure from motion, time-of-flight, terrestrial laser scanning or structured light approaches enable the assessment of plant traits such as leaf width and length, plant size, volume and development on plant and organ level. The introduced traits were shown with respect to the measured plant types, the used measuring technique and the link to their biological use case. These were trait and growth analysis for measurements over time as well as more complex investigation on water budget, drought responses and QTL (quantitative trait loci) analysis. The used processing pipelines were generalized in a 3D point cloud processing workflow showing the single processing steps to derive plant parameters on plant level, on organ level using machine learning or over time using time series measurements. Finally the next step in plant sensing, the fusion of different sensor types namely 3D and spectral measurements is introduced by an example on sugar beet. This multi-dimensional plant model is the key to model the influence of geometry on radiometric measurements and to correct it. This publication depicts the state of the art for 3D measuring of plant traits as they were used in plant phenotyping regarding how the data is acquired, how this data is processed and what kind of traits is measured at the single plant, the miniplot, the experimental field and the open field scale. Future research will focus on highly resolved point clouds on the experimental and field scale as well as on the automated trait extraction of organ traits to track organ development at these scales.
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Affiliation(s)
- Stefan Paulus
- Institute of Sugar Beet Research, Holtenser Landstr. 77, 37079 Göttingen, Germany
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41
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Kellner JR, Albert LP, Burley JT, Cushman KC. The case for remote sensing of individual plants. AMERICAN JOURNAL OF BOTANY 2019; 106:1139-1142. [PMID: 31469408 DOI: 10.1002/ajb2.1347] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 06/29/2019] [Indexed: 06/10/2023]
Affiliation(s)
- James R Kellner
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, 02912, USA
- Institute at Brown for Environment and Society, Brown University, Providence, RI, 02912, USA
| | - Loren P Albert
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, 02912, USA
- Institute at Brown for Environment and Society, Brown University, Providence, RI, 02912, USA
| | - John T Burley
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, 02912, USA
- Institute at Brown for Environment and Society, Brown University, Providence, RI, 02912, USA
| | - K C Cushman
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, 02912, USA
- Institute at Brown for Environment and Society, Brown University, Providence, RI, 02912, USA
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Fischer FJ, Maréchaux I, Chave J. Improving plant allometry by fusing forest models and remote sensing. THE NEW PHYTOLOGIST 2019; 223:1159-1165. [PMID: 30897214 DOI: 10.1111/nph.15810] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Accepted: 03/05/2019] [Indexed: 06/09/2023]
Abstract
Allometry determines how tree shape and function scale with each other, related through size. Allometric relationships help scale processes from the individual to the global scale and constitute a core component of vegetation models. Allometric relationships have been expected to emerge from optimisation theory, yet this does not suitably predict empirical data. Here we argue that the fusion of high-resolution data, such as those derived from airborne laser scanning, with individual-based forest modelling offers insight into how plant size contributes to large-scale biogeochemical processes. We review the challenges in allometric scaling, how they can be tackled by advances in data-model fusion, and how individual-based models can serve as data integrators for dynamic global vegetation models.
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Affiliation(s)
- Fabian Jörg Fischer
- Laboratoire Evolution et Diversité Biologique, UMR5174, CNRS-Université Paul Sabatier-IRD, Bâtiment 4R1, 118 route de Narbonne, F-31062, Toulouse Cedex 9, France
| | - Isabelle Maréchaux
- AMAP, INRA, IRD, CIRAD, CNRS, University of Montpellier, F-34000, Montpellier, France
| | - Jérôme Chave
- Laboratoire Evolution et Diversité Biologique, UMR5174, CNRS-Université Paul Sabatier-IRD, Bâtiment 4R1, 118 route de Narbonne, F-31062, Toulouse Cedex 9, France
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